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Proceedings Paper

Support vector machines and target classification
Author(s): Robert E. Karlsen; David J. Gorsich; Grant R. Gerhart
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Paper Abstract

The area of automatic target classification has been a difficult problem for many years. Many approaches involve extracting information from imagery through a variety of statistical filtering and sampling techniques, resulting in a reduced dimension feature vector that is the input for a learning algorithm. The Support Vector Machine (SVM) algorithm is a wide margin classifier that provides reasonable results for sparse data sets. This can allow one to avoid the feature extraction step and process images directly. The SVM algorithm has the additional benefits that there are few parameters to adjust and the solutions are unique for a given training set. We applied SVM to a variety of data sets, including character recognition, military vehicles and Synthetic Aperture Radar data, and compared the results to some standard neural network architectures. It was found that the SVM algorithm gave equivalent or higher correct classification results compared to the neural networks with some noted advantages.

Paper Details

Date Published: 17 August 2000
PDF: 11 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395553
Show Author Affiliations
Robert E. Karlsen, U.S. Army Tank-Automotive and Armaments Command (United States)
David J. Gorsich, U.S. Army Tank-Automotive and Armaments Command (United States)
Grant R. Gerhart, U.S. Army Tank-Automotive and Armaments Command (United States)

Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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